Motion Sensor Room Type Determination via Pattern Matching
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Solution Overview
Problem
Existing building management systems lack automation in determining room types based on usage patterns, leading to inefficient management of services like lighting and climate control, which can impact comfort and energy efficiency.
Innovation Solution
A system utilizing motion sensors, data aggregation, and machine learning to determine room types by analyzing motion patterns and comparing them to reference signatures, allowing for automated adjustment of operating parameters such as lighting and temperature settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual assignment of room types is used, then initial setup and configuration can be performed, but ongoing management and adaptation to changing usage patterns becomes inefficient and unresponsive
Solution Approach 1:
The system enables automatic self-determination of room types by motion sensors analyzing occupancy patterns without requiring manual intervention from facility managers. The sensors continuously monitor and the system autonomously classifies room types based on observed usage behaviors, freeing manual resources for higher-value tasks.
Solution Approach 2:
The system implements continuous feedback loops where motion sensor data is collected, analyzed, and used to update room type classifications in real-time. This feedback mechanism allows the system to adapt to changing usage patterns and provide dynamic room management insights without manual reconfiguration.
2Adaptability or versatility
If room settings are manually adjusted, then initial configuration can be set, but ongoing optimization based on actual usage patterns is lost
Solution Approach 1:
The system transitions from static manual configuration to dynamic automated adjustment. Room settings such as lighting, temperature, and occupancy alerts are automatically modified based on real-time motion sensor data and detected usage patterns, allowing the environment to adapt flexibly to changing needs without manual intervention.
Solution Approach 2:
The system performs preliminary analysis of motion patterns and usage behaviors to proactively determine optimal room settings before manual intervention is needed. By anticipating usage trends and pre-adjusting parameters, the system eliminates the need for reactive manual reconfiguration and saves time.
3Measurement precision
If motion sensors continuously monitor room usage, then accurate real-time data is collected, but energy consumption increases
Solution Approach 1:
The motion sensors operate in periodic cycles, alternating between active monitoring and low-power states. The system collects motion data at optimized intervals sufficient to capture usage patterns while minimizing continuous operation time, thereby reducing energy consumption while maintaining measurement accuracy for room type determination.
Data Source
AI summary
An example of an apparatus to determine a room type is provided. The apparatus includes a sensor to detect a motion event in a space. In addition, the apparatus includes a memory storage unit to store data associated with the motion event. Furthermore, the apparatus includes a communications interface to communicate with an external device. The communications interface is to transmit the local dataset to the external device and to receive an external dataset from the external device to be stored in the memory storage unit. The apparatus also includes an aggregator in communication with the memory storage unit to combine the local dataset with the external dataset to generate a motion signature. The apparatus further includes a matching engine to select a room type based on the motion signature.